{
  "abstract": "The short-time Fourier transform (STFT) characterizes the local frequency content of time-varying signals by evaluating the discrete Fourier transform over a sliding analysis window. The STFT is governed by a fundamental resolution trade-off: a short window localizes rapid transients in time at the cost of frequency resolution, whereas a long window resolves closely spaced harmonics at the expense of temporal resolution. The Universal Short-Time Fourier Transform (U-STFT) resolves this trade-off sequentially by modeling the analysis window duration as a data-adaptive state variable that indexes the number of hops included in the current observation interval. At each frame, a bank of candidate window durations is scored using normalized order-three Rényi entropy to quantify joint time-frequency concentration. A window length trellis aggregates the resulting $\\mathcal{O}(3^M)$ candidate window schedules with linear complexity per frame, guaranteeing a cumulative mix-loss regret bound of $\\mathcal{O}((k+1)\\log M)$ relative to any hindsight window schedule with $k$ resolution changes over $M$ frames. By linearity of the Fourier transform, posterior expectation across the candidate bank yields a single spectrum equivalent to an STFT evaluated with a data-adaptive effective window, while an envelope-normalized synthesis stream guarantees exact overlap-add reconstruction. Evaluations on synthetic and marine bioacoustic signals demonstrate adaptive resolution tracking, enhanced spectral concentration, and exact time-domain invertibility.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.37795",
  "author_total": 5,
  "authors": [
    {
      "name": "Manan Mittal"
    },
    {
      "name": "Kanad Sarkar"
    },
    {
      "name": "Ryan M. Corey"
    },
    {
      "name": "John R. Buck"
    },
    {
      "name": "Andrew C. Singer"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dj6e4p8fy5hyr.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.37795",
  "fulltext_url": null,
  "id": "dj6e4p8fy5hyr",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.37795",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.IT"
    ],
    [
      "arxiv",
      "cs.SD"
    ],
    [
      "arxiv",
      "eess.AS"
    ],
    [
      "arxiv",
      "eess.SP"
    ],
    [
      "arxiv",
      "math.IT"
    ],
    [
      "arxiv",
      "stat.ML"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.37795",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dj6e4p8fy5hyr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:32Z",
      "native_id": "2610.37795",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.37795"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Universal Short-Time Fourier Transform: Data-Adaptive Window-Length Selection for Time-Frequency Analysis",
  "title_text": "Universal Short-Time Fourier Transform: Data-Adaptive Window-Length Selection for Time-Frequency Analysis",
  "type": "preprint",
  "updated_at": "2026-11-01T12:21:00Z",
  "url": "https://research.draconitic.com/paper/dj6e4p8fy5hyr",
  "venue": "arXiv",
  "versions": []
}